Anomaly Detection in Financial Time Series Data using Autoencoders

Detecting anomalies in time series data is of utmost importance. Anomalies can indicate fraudulent activities, market manipulation, or other irregularities that can have significant financial implications. Traditional statistical methods for anomaly detection may not be sufficient to capture complex patterns in financial data. This is where autoencoders, a type of neural network, come into play.

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Autoencoders are unsupervised learning models that are capable of learning efficient representations of input data. They consist of an encoder network that maps the input data to a lower-dimensional latent space and a decoder network that reconstructs the input data from the latent space. By training an autoencoder on normal financial time series data, we can use it to detect anomalies by measuring the reconstruction error.

In this tutorial, we will explore how to perform anomaly detection in financial time series data using autoencoders. We will start by obtaining financial data using the yfinance library, preprocess the data, build and train an autoencoder model and finally, use the trained model to detect anomalies in the data.

Downloading Financial Data

To begin, we need to download financial data for a set of assets. We will use the yfinance…